Integrating External Benchmarking for Machine Performance

Abstract

Manufacturing companies rely on internal performance targets to evaluate ma- chine efficiency, yet these targets are rarely validated against external industry levels, leaving it unclear whether performance ambitions reflect competitive stan- dards. This thesis develops an external benchmarking initiative for evaluating machine performance through OEE-related KPIs, specifically speed efficiency, time efficiency, and material efficiency, by collecting external data via machine suppliers and adapting it to machine-specific operational conditions. The study was conducted as a case study at a multinational manufacturing com- pany operating seven European production plants, each running a diverse prod- uct portfolio on the same type of machine. Data was collected through interviews, observations, and extraction of internal performance data, and was analyzed in relation to supplier-provided industry reference levels. The findings show that speed efficiency could be fully benchmarked externally by accounting for product mix through a scenario-based weighting approach, cat- egorizing products into complexity levels and weighting them by their share of production run time. Time efficiency and material efficiency could not be bench- marked at a complete KPI level due to data limitations, but individual compo- nents could still be compared against supplier-provided percentages to calculate component-level indices. A simulation-based integration approach was developed, where external bench- marking data is translated into each machine’s own operational context. This allows performance gaps to be identified relative to industry reference levels. The initiative also provides a foundation for challenging internally defined performance targets with externally validated benchmarks. The developed benchmarking ini- tiative is intended to be applicable to the manufacturing industry more broadly.